AIM-AHEAD DICB Awardee Launches aiMATCH Leukemia Patient Outcome Prediction Data Challenge
The aiMATCH team is preparing to launch the new Leukemia Patient Outcome Prediction Challenge in partnership with AIM-AHEAD, The University of Texas at San Antonio, and MD Anderson Cancer Center. The challenge is now live as of December 17, 2025, and will run until Friday, February 20, 2026, at 11:59 PM CT (GMT -06:00). All members registered on AIM-AHEAD Connect, including researchers, trainees, awardees, and other participants, are invited to develop algorithms that predict patient outcomes using a comprehensive, multimodal leukemia dataset.
This new challenge expands on the earlier Acute Myeloid Leukemia Outcome Prediction Challenge, which invited participants to model patient outcomes using novel computational approaches. For the upcoming launch, the aiMATCH team is again supporting participants by preparing the platform with relevant scientific literature to help inform modeling approaches and deepen understanding of leukemia biology. The aiMATCH chatbot is also being refined to provide guidance about dataset features, modeling strategies, and other technical questions.
The aiMATCH Leukemia Dataset powering this challenge is a multi-omic, multi-cohort resource that integrates clinical, genomic, cytogenetic, and reverse-phase protein array proteomic measurements across pediatric and adult patients. This unique structure offers participants the opportunity to explore novel relationships and uncover new biological insights across multiple molecular layers. By working with such a rich and comprehensive dataset, participants can strengthen their modeling and analytical skills while contributing to real-world research questions.
To support learning and collaboration, the AIM-AHEAD Data Infrastructure and Capacity Building (DICB) program will offer a companion course focused on technical and modeling skills relevant to the challenge. Participants will also have access to consultation and discussion forums throughout the competition, a midpoint check-in session with Dr. Kornblau, and an interactive chatbot designed to help users better understand and navigate the dataset.
Don’t miss out on this exciting opportunity to apply computational modeling to a rich, multimodal leukemia dataset and contribute to real-world research! Click here to register, or find more details on the Data Challenge website.